{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-06-06T10:04:21.525287Z",
     "start_time": "2024-06-06T10:04:21.502895Z"
    }
   },
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x, y = load_digits(return_X_y=True)  #载入数据\n",
    "x = x.reshape(-1, 8, 8, 1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=202406)  #划分训练集与测试集\n",
    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1437, 8, 8, 1) (360, 8, 8, 1) (1437,) (360,)\n"
     ]
    }
   ],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:04:21.530497Z",
     "start_time": "2024-06-06T10:04:21.526296Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "#对标签进行独热编码\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n"
   ],
   "id": "e8ce0a5179d2c832",
   "outputs": [],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:04:21.584384Z",
     "start_time": "2024-06-06T10:04:21.531505Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras import models, layers\n",
    "\n",
    "#定义网络结构\n",
    "CNN = models.Sequential()\n",
    "\n",
    "#定义卷积层\n",
    "#该有32个3x3的卷积核，步长为1\n",
    "CNN.add(layers.Convolution2D(input_shape=(8, 8, 1), filters=32, kernel_size=3, strides=1, padding='same',\n",
    "                             activation='relu'))\n",
    "#定义池化层\n",
    "CNN.add(layers.MaxPool2D(pool_size=2, strides=2, padding='same'))\n",
    "CNN.add(layers.Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu'))\n",
    "CNN.add(layers.MaxPool2D(pool_size=2, strides=2, padding='same'))\n",
    "#扁平化\n",
    "CNN.add(layers.Flatten())\n",
    "#全连接层\n",
    "CNN.add(layers.Dense(units=512, activation='relu'))\n",
    "CNN.add(layers.Dropout(0.5))\n",
    "CNN.add(layers.Dense(units=10, activation='softmax'))"
   ],
   "id": "1c196d1f4d90005f",
   "outputs": [],
   "execution_count": 59
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:04:22.798432Z",
     "start_time": "2024-06-06T10:04:21.586394Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#绘制网络结构示意图\n",
    "plot_model(CNN, to_file='CNN.png', show_shapes=True, show_layer_names=False, rankdir='TB')\n",
    "plt.figure(figsize=(10, 10))\n",
    "img = plt.imread('CNN.png')\n",
    "plt.imshow(img)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ],
   "id": "74281d5d50369a5f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 60
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:04:26.685501Z",
     "start_time": "2024-06-06T10:04:22.799438Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras.optimizers import Adam\n",
    "\n",
    "#设置训练参数并进行训练\n",
    "#设置学习率为0.001\n",
    "CNN.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "#进行20次训练，每批64个数据\n",
    "CNN.fit(x_train, y_train, epochs=20, batch_size=64)\n"
   ],
   "id": "c0d647f79d70ddbc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step - accuracy: 0.2729 - loss: 2.3043\n",
      "Epoch 2/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.8464 - loss: 0.6093\n",
      "Epoch 3/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.9273 - loss: 0.2343\n",
      "Epoch 4/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9544 - loss: 0.1485\n",
      "Epoch 5/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9711 - loss: 0.0961\n",
      "Epoch 6/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9874 - loss: 0.0566\n",
      "Epoch 7/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.9843 - loss: 0.0592\n",
      "Epoch 8/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.9893 - loss: 0.0375\n",
      "Epoch 9/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9925 - loss: 0.0344\n",
      "Epoch 10/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.9974 - loss: 0.0184\n",
      "Epoch 11/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9929 - loss: 0.0395\n",
      "Epoch 12/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9952 - loss: 0.0234\n",
      "Epoch 13/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9968 - loss: 0.0180\n",
      "Epoch 14/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.9964 - loss: 0.0162\n",
      "Epoch 15/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9987 - loss: 0.0110\n",
      "Epoch 16/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9968 - loss: 0.0157\n",
      "Epoch 17/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9963 - loss: 0.0147\n",
      "Epoch 18/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.9990 - loss: 0.0054\n",
      "Epoch 19/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.9963 - loss: 0.0080\n",
      "Epoch 20/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.9968 - loss: 0.0078\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1e433169730>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:10:20.332599Z",
     "start_time": "2024-06-06T10:10:20.015470Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "#进行模型评估\n",
    "train_loss, train_acc = CNN.evaluate(x_train, y_train, batch_size=64)\n",
    "print(f\"训练集损失函数{train_loss:.4f},准确率{train_acc * 100:.2f}%\")\n",
    "test_loss, test_acc = CNN.evaluate(x_test, y_test, batch_size=64)\n",
    "print(f\"测试集损失函数：{test_loss:.4f},准确率：{test_acc * 100:.2f}%\")\n",
    "y_pred = CNN.predict(x_test).argmax(axis=-1)\n",
    "y_true = y_test.argmax(axis=-1)\n",
    "print(\"-----分类报告如下-----\")\n",
    "print(classification_report(y_true, y_pred))\n",
    "print(\"混淆矩阵如下\")\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "print(cm)\n",
    "print(\"-----网络报告如下-----\")\n",
    "print(CNN.summary())"
   ],
   "id": "9cc5beb0f7229f8a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 1.0000 - loss: 6.9465e-04 \n",
      "训练集损失函数0.0008,准确率100.00%\n",
      "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.9945 - loss: 0.0158 \n",
      "测试集损失函数：0.0173,准确率：99.44%\n",
      "\u001B[1m12/12\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step \n",
      "-----分类报告如下-----\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98        30\n",
      "           1       1.00      1.00      1.00        33\n",
      "           2       1.00      1.00      1.00        42\n",
      "           3       1.00      1.00      1.00        35\n",
      "           4       0.95      1.00      0.97        35\n",
      "           5       1.00      0.97      0.99        38\n",
      "           6       1.00      1.00      1.00        36\n",
      "           7       1.00      1.00      1.00        40\n",
      "           8       1.00      1.00      1.00        38\n",
      "           9       1.00      1.00      1.00        33\n",
      "\n",
      "    accuracy                           0.99       360\n",
      "   macro avg       0.99      0.99      0.99       360\n",
      "weighted avg       0.99      0.99      0.99       360\n",
      "\n",
      "混淆矩阵如下\n",
      "[[29  0  0  0  1  0  0  0  0  0]\n",
      " [ 0 33  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 42  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 35  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 35  0  0  0  0  0]\n",
      " [ 0  0  0  0  1 37  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 36  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 40  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 38  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 33]]\n",
      "-----网络报告如下-----\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1mModel: \"sequential_10\"\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_20 (\u001B[38;5;33mConv2D\u001B[0m)              │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m8\u001B[0m, \u001B[38;5;34m8\u001B[0m, \u001B[38;5;34m32\u001B[0m)       │           \u001B[38;5;34m320\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_20 (\u001B[38;5;33mMaxPooling2D\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m4\u001B[0m, \u001B[38;5;34m4\u001B[0m, \u001B[38;5;34m32\u001B[0m)       │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_21 (\u001B[38;5;33mConv2D\u001B[0m)              │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m4\u001B[0m, \u001B[38;5;34m4\u001B[0m, \u001B[38;5;34m64\u001B[0m)       │        \u001B[38;5;34m18,496\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_21 (\u001B[38;5;33mMaxPooling2D\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m2\u001B[0m, \u001B[38;5;34m2\u001B[0m, \u001B[38;5;34m64\u001B[0m)       │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_10 (\u001B[38;5;33mFlatten\u001B[0m)            │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m256\u001B[0m)            │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_20 (\u001B[38;5;33mDense\u001B[0m)                │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m512\u001B[0m)            │       \u001B[38;5;34m131,584\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (\u001B[38;5;33mDropout\u001B[0m)            │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m512\u001B[0m)            │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_21 (\u001B[38;5;33mDense\u001B[0m)                │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m10\u001B[0m)             │         \u001B[38;5;34m5,130\u001B[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,584</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,130</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m155,532\u001B[0m (607.55 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">155,532</span> (607.55 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m155,530\u001B[0m (607.54 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">155,530</span> (607.54 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Optimizer params: \u001B[0m\u001B[38;5;34m2\u001B[0m (12.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (12.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:10:30.973323Z",
     "start_time": "2024-06-06T10:10:30.318410Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# 绘制混淆矩阵的热力图\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.arange(10), yticklabels=np.arange(10))\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ],
   "id": "abc10e7de448026e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 76
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:04:27.741884Z",
     "start_time": "2024-06-06T10:04:27.604538Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "#随机展示一个样例\n",
    "index = np.random.randint(0, len(x_test))\n",
    "sample = x_test[index]\n",
    "sample = sample.reshape(8, 8)\n",
    "plt.imshow(sample, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.title(f\"True Label:{y_true[index]},Predicted Label:{y_pred[index]}\")\n",
    "plt.show()\n",
    "print(np.argmax(y_pred[index]))"
   ],
   "id": "5fa5bdb61a92aa11",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:07:19.513279Z",
     "start_time": "2024-06-06T10:07:19.487414Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#测试完毕，保存网络模型\n",
    "CNN.save('CNN.h5')"
   ],
   "id": "2b59b8a43c2be553",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "execution_count": 66
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
